How can I incrementally train a Yolo model without catastrophic forgetting?
There's something called Elastic Weight Consolidation to prevent neural networks from forgetting previous tasks as they train on new tasks. It might be helpful for your case too.

The main idea is to quantify the importance of parameters for task $t$ and penalize the model in proportion when it changes its parameters as it trains to learn task $t+1$. As you can see, this incentivizes model to change parameters that are less important for task $t$ which prevents the model from forgetting it.